Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology

Multiple instance learning (MIL) is the most widely used framework in computational pathology encompassing sub-typing diagnosis prognosis and more. However the existing MIL paradigm typically requires an offline instance feature extractor such as a pre-trained ResNet or a foundation model. This approach lacks the capability for feature fine-tuning within the specific downstream tasks limiting its adaptability and performance. To address this issue we propose a Re-embedded Regional Transformer (RRT) for re-embedding the instance features online which captures fine-grained local features and establishes connections across different regions. Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator RRT is tailored to re-embed instance features online. It serves as a portable module that can seamlessly integrate into mainstream MIL models. Extensive experimental results on common computational pathology tasks validate that: 1) feature re-embedding improves the performance of MIL models based on ResNet-50 features to the level of foundation model features and further enhances the performance of foundation model features; 2) the RRT can introduce more significant performance improvements to various MIL models; 3) RRT-MIL as an RRT-enhanced AB-MIL outperforms other latest methods by a large margin. The code is available at: https://github.com/DearCaat/RRT-MIL.